Automated system for training and assessing reaching and grasping behaviors in rodents.

BACKGROUND: Reaching, grasping, and pulling behaviors are studied across species to investigate motor control and problem solving. String pulling is a distinct reaching and grasping behavior that is rapidly learned, requires bimanual coordination, is ethologically grounded, and has been applied across species and disease conditions. NEW METHOD: Here we describe the PANDA system (Pulling And Neural Data Analysis), a hardware and software system that integrates a continuous string loop connected to a rotary encoder, feeder, microcontroller, high-speed camera, and analysis software for the assessment and training of reaching, grasping, and pulling behaviors and synchronization with neural data. RESULTS: We demonstrate this system in rats implanted with electrodes in motor cortex and hippocampus and show how it can be used to assess relationships between reaching, pulling, and grasping movements and single-unit and local-field activity. Furthermore, we found that automating the shaping procedure significantly improved performance over manual training, with rats pulling > 100 m during a 15-minute session. COMPARISON WITH EXISTING METHODS: String-pulling is typically shaped by tying food reward to the string and visually scoring behavior. The system described here automates training, streamlines video assessment with deep learning, and automatically segments reaching movements into distinct reach/pull phases. No system, to our knowledge, exists for the automated shaping and assessment of this behavior. CONCLUSIONS: This system will be of general use to researchers investigating motor control, motivation, sensorimotor integration, and motor disorders such as Parkinson's disease and stroke.

Automated system for training and assessing string-pulling behaviors in rodents.

String-pulling tasks have been used for centuries to study coordinated bimanual motor behavior and problem solving. String pulling is rapidly learned, ethologically grounded, and has been applied to many species and disease conditions. Typically, training of string-pulling behaviors is achieved through manual shaping and baiting. Furthermore, behavioral assessment of reaching, grasping, and pulling is often performed through labor intensive manual video scoring. No system, to our knowledge, currently exists for the automated shaping and assessment of string-pulling behaviors. Here we describe the PANDA system (Pulling And Neural Data Analysis), an inexpensive hardware and software system that utilizes a continuous string loop connected to a rotary encoder, feeder, microcontroller, high-speed camera, and analysis software for assessment and training of string-pulling behaviors and synchronization with neural recording data. We demonstrate this system in unimplanted rats and rats implanted with electrodes in motor cortex and hippocampus and show how the PANDA system can be used to assess relationships between paw movements and single-unit and local-field activity. We also found that automating the shaping procedure significantly improved overall performance, with rats regularly pulling >100 meters during a 15-minute session. In conclusion, the PANDA system will be of general use to researchers investigating motor control, motivation, and motor disorders such as Parkinson's disease, Huntington's disease, and stroke. It will also support the investigation of neural mechanisms involved in sensorimotor integration.

The efficacy of somatosensory evoked potentials in evaluating new neurological deficits after spinal thoracic fusion and decompression.

OBJECTIVE: Posterior thoracic fusion (PTF) is used as a surgical treatment for a wide range of pathologies. The monitoring of somatosensory evoked potentials (SSEPs) is used to detect and prevent injury during many neurological surgeries. The authors conducted a study to evaluate the efficacy of SSEPs in predicting perioperative lower-extremity (LE) neurological deficits during spinal thoracic fusion surgery. METHODS: The authors included patients who underwent PTF with SSEP monitoring performed throughout the entire surgery from 2010 to 2015 at the University of Pittsburgh Medical Center (UPMC). The sensitivity, specificity, odds ratio, and receiver operating characteristic curve were calculated to evaluate the diagnostic accuracy of SSEP changes in predicting postoperative deficits. Univariate analysis was completed to determine the impact of age exceeding 65 years, sex, obesity, abnormal baseline testing, surgery type, and neurological deficits on the development of intraoperative changes. RESULTS: From 2010 to 2015, 771 eligible patients underwent SSEP monitoring during PTF at UPMC. Univariate and linear regression analyses showed that LE SSEP changes significantly predicted LE neurological deficits. Significant changes in LE SSEPs had a sensitivity and specificity of 19% and 96%, respectively, in predicting LE neurological deficits. The diagnostic odds ratio for patients with new LE neurological deficits who had significant changes in LE SSEPs was 5.86 (95% CI 2.74-12.5). However, the results showed that a loss of LE waveforms had a poor predictive value for perioperative LE deficits (diagnostic OR 1.58 [95% CI 0.19-12.83]). CONCLUSIONS: Patients with new postoperative LE neurological deficits are 5.9 times more likely to have significant changes in LE SSEPs during PTF. Surgeon awareness of an LE SSEP loss may alter surgical strategy and positively impact rates of postoperative LE neurological deficit status. The relatively poor sensitivity of LE SSEP monitoring may indicate a need for multimodal neurophysiological monitoring, including motor evoked potentials, in thoracic fusion surgery.